uncertainty-quantification

This paper introduces Transported Conformal Calibration (TCC), a method to transfer conformal prediction calibration from a labeled source domain to an unlabeled target domain using paired observations, ensuring coverage guarantees with label-free diagnostics.

CUSP is a training-free framework for quantifying collective uncertainty in multi-agent multimodal systems using semantic opinion pooling, improving reliability and accuracy over baseline methods.

IXPLORE is a bounded ideal point estimation algorithm that improves reconstruction and imputation performance in political data analysis, particularly for sparse responses, with grid-based uncertainty quantification.

This paper proposes a deep reinforcement learning framework for risk and anomaly identification in distribution networks, using uncertainty quantification to distinguish between inherent risks and out-of-distribution anomalies.

OutageDiT is a generative foundation model for power outage forecasting that uses a Diffusion Transformer architecture to generate seven-day outage trajectories, improving forecast accuracy and enabling zero-shot transfer to new regions.

The paper proposes EPA-CarbonNet, a six-layer transformer architecture for carbon credit price prediction that integrates market data and policy text, but tests on S&P carbon index data show mixed results with a random walk outperforming on some metrics while directional accuracy is promising.

AdaptNTK introduces an adaptive uncertainty quantification framework using neural tangent kernels to enable efficient active learning in neural network potentials, reducing computational cost while maintaining accuracy in molecular dynamics simulations.

This paper presents a method combining neural network means with exact Matérn kernel corrections for operator learning in PDEs, achieving competitive or improved performance on public benchmarks like structural mechanics and OCO-2 radiative transfer emulation.

This paper proposes a calibration method for product-of-experts Gaussian process models to correct overestimated posterior variances, enhancing uncertainty quantification and scalability for large datasets.

This paper proposes a hybrid semantic context-enhanced ensemble learning approach for wind power ramp-event forecasting, demonstrating small but statistically significant improvements over baseline models with uncertainty-aware evaluation.

This paper introduces a probabilistic approach to end-to-end AI weather forecasting by enhancing the Aardvark Weather model with learned observation noise and Monte Carlo dropout, improving mean forecasts by 4.2% on average and separating aleatoric and epistemic uncertainty.

This paper introduces a framework for E3-equivariant uncertainty quantification in tensor-valued geometric learning, ensuring symmetric positive-definite covariances via matrix exponentiation and proposing a robust Log-Euclidean Equivariant Scoring Objective.

The paper introduces Latent-Posterior Alignment to explain uncertainty reduction in Graph Neural Networks with Bayesian output layers and proposes Alignment-Guided Learning to improve uncertainty quantification and model calibration.

SPARC introduces a Bayesian–conformal uncertainty layer for motion forecasting, achieving first rank in NLL and combined criteria with efficient, calibrated uncertainty estimates.

This paper presents a method to improve confidence estimates for black-box large language models by building classifiers that predict response correctness, outperforming existing zero-shot methods with minimal computational overhead.

The paper introduces DRACP, a conformal prediction method that unifies multiple adaptation mechanisms for reliable economic forecast intervals under distribution shifts, achieving calibration with wider intervals.

The paper proposes RATTL, a framework that adjusts an agent's caution based on its Bayesian belief uncertainty using Wasserstein distance for safe sequential decision making, applicable to LLM-based systems.

This survey organizes research on uncertainty-aware decision making in multimodal large language models, covering sources of uncertainty, calibration methods, and actions to improve system reliability and safety.

This paper introduces a data-driven framework combining supervised classification and generative modeling to reconstruct separatrices in multistable dynamical systems, using neural networks and score-based generative models to approximate boundaries of basins of attraction.

This paper systematically evaluates uncertainty-aware decoding with rollback mechanisms to improve code generation in large language models, demonstrating performance gains over standard methods using uncertainty signals.